class: title-slide <style>.xe__progress-bar__container { bottom:0; opacity: 1; position:absolute; right:0; left: 0; } .xe__progress-bar { height: 0.25em; background-color: #004c93; width: calc(var(--slide-current) / var(--slide-total) * 100%); } .remark-visible .xe__progress-bar { animation: xe__progress-bar__wipe 200ms forwards; animation-timing-function: cubic-bezier(.86,0,.07,1); } @keyframes xe__progress-bar__wipe { 0% { width: calc(var(--slide-previous) / var(--slide-total) * 100%); } 100% { width: calc(var(--slide-current) / var(--slide-total) * 100%); } }</style> <br> <br> # A Data Mining Approach for Detecting Collusion in Unproctored Online Exams<br> .padding_left.pull-down.white[ J. Langerbein, T. Massing, .bold[_J. Klenke_], M. Striewe, M. Goedicke, C. Hanck, N. Reckmann <br> <br> <br> `\(15^{th}\)` International Conference on Educational Data Mining Bangalore, 11-24 July, 2023 ]